Thermal analysis of mineral oil‐based nanofluids of distribution transformers exposed to simultaneous current and voltage harmonics
Bibliographic record
Abstract
Abstract The exact thermal evaluation of distribution transformers (DTs), which are critical and costly pieces of equipment for the power grids, may contribute to preventing the respective failures. Therefore, the present study non‐uniformly investigated DT for correct anticipation of hotspot temperature (HST). Optical fibre sensors (OFSs) were applied for assessing our newly developed non‐uniform 3D computational fluid dynamic (CFD)‐based modelling while performing the temperature rise test (TRT). It should be noted that this new 3D CFD‐based thermal analysis showed an error percentage of 0.11% (0.1°C) in comparison to the OFS measurement, reflecting the ideal efficiency and accuracy of the model. Moreover, thermography for both top‐oil temperature (TOT) and bottom‐oil temperature (BOT) was employed to validate the results from non‐uniform 3D (three‐dimensional) CFD‐based thermal evaluations. The results indicated an acceptable level of relationship between thermography and thermal analysis of 3D CFD at the specified two spots, with an error percentage of <0.65%, demonstrating the acceptable accuracy of the new non‐uniform 3D CFD‐based model. In the following, yet importantly, the new non‐uniform 3D model was subjected to the total harmonic distortions (THD) for the current and voltage of 5%, 10%, and 15%, which raised the HST more than the original model without harmonics by 3.3°C, 7.1°C, and 10.3°C, respectively. Ultimately, different mineral oil‐based nanofluids’, such as multi‐walled carbon nanotubes (MWCNTs) and diamond nanoparticles, influence on the HST decrement of DT in simultaneous current and voltage harmonics was investigated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".